An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most correspondence
methods first extract descriptors of salient features from robot sensor data, then matches between features are searched and finally the transformation that relates the maps is estimated
from such matches. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a bag of features approach to identify putative corresponding maps followed by a
Gauss-Newton algorithm to find the transformation that relates both maps. The proposed method is evaluated on a typical office dataset showing good performance.This work has been partially funded by TIN 2006-15308-C02-02 project grant of the Ministry of Education of Spain
